ACL(WS)-2022-RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction

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RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction

Summary:

大部分研究忽视了relation和subject, objects的语义信息，本研究提出的模型，首先提取潜在的realtions，然后将lretions转化为relation templates，最后为了填补templates里的空白，找到对应的subject和objects

Resource:

• pdf
• lizhe2016/rfbfn
• [paper-with-code](

Paper information:

• Author:
• Dataset: NYT WebNLG NYT WebNLG
• keywords:

Notes:

Model Graph:

Result:：

Thoughts:

是不是数据集太简单了，只要用上了BERT，评分都到90了

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